Precision Medicine AI Explained: What It Means for Your Healthcare Business

If the current excitement is any indication, we are close to cardinal changes in healthcare. For decades, healthcare was dependent on a generic approach to treatment because there was no viable way to customize care on an individual level. However, the landscape has dramatically shifted. With the explosion of healthcare data and the swift progress in artificial intelligence (AI) and big data analytics, the dream of precision medicine AI that can tailor treatments to each patient’s health profile is now within reach. Excitement is great, but let’s zoom in on the practicalities.

Technology implementation is just one part of the equation if your goal is to deliver personalized medicine powered by AI. It also involves significant changes in your clinical practices and, most of all, in data management. As someone who has successfully delivered AI development services to many healthcare organizations, we want to share our insights. We’ll discuss the most impactful applications of AI in precision medicine to show how your practice can benefit from this technology. 

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What is Precision Medicine AI?

Traditional medicine often relies on a “population-based” approach, which means it treats diseases based on average patient responses. Precision medicine, however, flips the script. It eagers to understand individual variations in genes, proteins, and other biological factors to predict and prevent disease or tailor treatments for maximum effectiveness.

Artificial intelligence makes its mark in many healthcare fields, and its role in precision medicine is equally game-changing. Thanks to AI’s capabilities to process complex datasets and identify trends that would be impossible for humans to detect, it allows doctors to:

• Predict disease risk. Genetic and lifestyle data analysis will let AI pinpoint individuals at higher risk for specific diseases. Knowing about it, healthcare providers can intervene earlier and potentially prevent the onset of illness altogether.
• Tailor treatment plans. AI can use a patient’s data to recommend the most effective treatment options that will maximize success rates and minimize side effects.
• Make more accurate diagnoses. AI systems have proved to be extremely precise at interpreting medical images and clinical data.
• Develop new drugs. AI helps accelerate drug discovery through the analysis of vast datasets that can determine the most promising drug targets tailored to specific genetic variations several times faster than traditional methods. 

Importance of Integrating AI with Precision Medicine

The AI usage for precision medicine promises to democratize access to personalized care and make it affordable to everyone. That’s the major and most weighty AI benefits in healthcare. Here are more details on why it’s worth considering AI integration:

1. AI Decodes the Human Genome

Traditional medical practices treat everyone the same, overlooking the inherent biological and genetic variations among individuals. Each person is unique, and the way their body interacts with pathogens or diseases varies from one individual to another. AI helps healthcare professionals interpret those unique human genomes. It examines a lot of genetic information to find patterns and differences within DNA that may indicate potential disease susceptibilities like diabetes or cancer. AI can also be used to predict how a patient’s body might respond to certain drugs and help doctors alter treatments appropriately.

2. AI Eliminates the Guesswork

Currently, diagnoses and treatment plans often rely on population averages and best practices. While this isn’t inherently bad, it fails to account for individual differences in physiology and response to medications. AI-driven personalized medicine removes this guesswork by providing precise, data-driven insights into each patient’s unique health profile. With AI, doctors can detect abnormalities specific to the individual and make more precise diagnoses and targeted treatment plans.

3. Affordable and Accessible Precision Medicine for All

One of the biggest challenges with precision medicine is cost and accessibility. Precision medicine needs a wealth of data – genetic information, medical history, lifestyle factors, and environmental exposures. The analysis of such large amounts of data is a labor-intensive process and requires significant human expertise. AI is the perfect solution for these tasks. As it automates data analysis and streamlines processes, which ultimately reduces the overall cost of delivering personalized medicine, it makes precision medicine more affordable. 

The Evolution of Precision Medicine AI

Interestingly, the journey to truly personalized care began with the very technologies that initially fueled a “one-dimensional” strategy – big data and genetic research. They offered broad population-level insights but lacked the granularity to truly tailor treatments to individual needs. Now, with the advent and rise of AI, the promise of bespoke healthcare finally becomes a reality. Let’s see how AI precision medicine was born and progressed to the state we know today. 

Historical Context and Milestones in Precision Medicine

• Early Efforts (1950s-1980s): The discovery of the DNA double helix marked a turning point. This finding, along with research into blood types and how bodies react to medications (HLA compatibility), started the journey toward personalized medicine. It showed that everyone’s body is a little different, and what works for one person might not work for another.
• The Rise of Personalized Medicine (1990s-2000s): The Human Genome Project (HGP) fueled a surge in genetic research and highlighted the importance of individual genomes in disease susceptibility and treatment response. Pharmacogenomics, the study of how genes influence drug response, emerged as a key area of focus.
• The Precision Medicine Revolution (2010s-Present): Next-generation sequencing (NGS) and the rise of AI ushered in a new era where tons of medical and genetic information can be processed quickly and cheaply. Where traditional sequencing technology took over a decade to produce results, NGS can sequence an entire human genome in just about 40 hours. 

Just like a super-powered microscope for genetics, AI sees the tiniest details about a person’s DNA and health. Quicker data insights at scale can streamline clinical trials, identify populations with a higher risk of developing certain diseases based on genetic predispositions, and so much more. 

Successful Implementations of AI in Precision Medicine

AI’s impact on precision medicine is already evident, with cancer treatment being one of the most effective. Memorial Sloan Kettering Cancer Center (MSK) uses AI to process large volumes of tumor mutation data and identify the specific genetic drivers of a patient’s cancer. With this information at hand, doctors can develop more targeted treatment options. 

Traditionally, treatments might focus on the general type of cancer (e.g., lung cancer), but AI can pinpoint the specific genetic variations within that cancer, which allows for a more precise approach. IBM’s Watson for Oncology is another powerful AI system that recommends personalized treatment plans for cancer patients that target only the cancer cells and minimize side effects. Studies show AI can detect breast cancer, especially the early and tricky kind, with a higher success rate (91%) compared to radiologists (74%). Similarly, AI has a sharper eye for masses, identifying 90% of them compared to radiologists’ 78%. 

Early detection of neurological disorders is another promising area where AI makes considerable contributions. Particularly, we talk about subtle signs of neurodegenerative diseases like Alzheimer’s and Parkinson’s. BrainScan AI and a number of other companies successfully develop and test AI models that detect these changes in brain structure and function earlier and more accurately than traditional methods.

Core Technologies in Precision Medicine AI

As you may know, AI is a generic term that encompasses various technologies and methods for creating intelligent machines and AI models. We want to review the most common ones used to create AI systems. Each of the AI technologies has unique capabilities and different applications we’re going to look closer at.

Machine Learning and Predictive Analytics

The rise of precision medicine can be largely attributed to machine learning (ML) and predictive analytics, which can work wonders with immense datasets of medical information to deliver actionable insights. 

So, machine learning algorithms are essentially computer programs that can learn from data without being explicitly programmed. In healthcare, these algorithms are trained on massive datasets of patient information (genetic data, patient medical history, lifestyle factors, and more) to find not-so-obvious relationships within data. The discovered patterns can then be used for a number of different purposes. 

Here, we also need to understand the different types of ML algorithms used in precision medicine AI, which are divided by their learning style:

• Supervised learning is the way an algorithm learns from labeled data, where the outcome is already known. In healthcare, this data might include patient information paired with confirmed diagnoses or treatment responses. The algorithm analyzes these examples and learns to identify patterns and relationships between the data points (genetic data, medical history, lifestyle factors) and the known outcomes (disease diagnosis, treatment response). Once trained, it can then use these learned patterns to predict outcomes for new, unseen data. Hence, it’s particularly effective for disease risk assessment and treatment response prediction tasks. 
• Unsupervised learning works by uncovering hidden patterns and groupings within the data itself. It doesn’t have labeled outcomes to guide it, so it relies on the inherent structure of the data to find associations. In the context of precision medicine, unsupervised learning is often used to cluster patients into subgroups based on genetic data. In other words, healthcare specialists use it for tasks like new disease subtypes discovery and patient stratification.

Now, let’s break down predictive analytics and see how precision medicine AI companies use it. 

Predictive analytics builds on machine learning. It leverages the insights and patterns learned by ML models to make predictions about future events. So, in healthcare, predictive analytics can:

• Detect disease early. ML-powered analytics can determine individuals at high risk of developing certain diseases, which allows doctors to take measures and start treatment earlier to prevent disease progression.
• Personalize treatment plans. Predictive models help doctors anticipate how a patient might respond to different medications or treatment options. As a result, care providers can tailor treatment plans according to individual needs and for better efficiency.

• Categorize population by risk. AI can categorize patients based on their risk of complications or adverse events. Such an approach lets hospitals allocate healthcare resources more strategically and monitor high-risk patients more closely.
• Predict hospital readmissions. Data from previous hospital stays analyzed by an AI-driven system will determine the patients most likely to be readmitted. So, doctors can intervene with targeted strategies to prevent them from returning to the hospital.
• Identify patients at risk of sepsis. Using a patient’s vital signs and lab results, ML can predict their risk of developing sepsis, a serious condition that can threaten life if not caught early, and make all the difference.

Genomics and AI

Genomics is the core of AI precision medicine. Genomic data is incredibly complex and involves billions of base pairs in the human DNA sequence. Conventional ways of processing this data are slow and often impractical for large-scale applications. That’s where AI’s ability to examine huge data volumes fast proved invaluable. 

With AI, genomics specialists can analyze genetic data more accurately and several times quicker, which makes research in this field more accessible and the development of new therapies speedier. An additional benefit of AI is that it can integrate genetic data with other types of medical data (electronic health records and lifestyle information) to find hidden connections that might be missed by looking at each data source in isolation.

So, how exactly can healthcare professionals use AI in their practice of genomic medicine?

• Disease prediction and prevention. AI can use the information on genetic predispositions to predict the likelihood of developing certain diseases. For instance, a patient’s genome will help AI detect markers that suggest a high risk of breast cancer or Alzheimer’s disease.
• Genetic counseling. AI tools assist genetic counselors by providing detailed risk assessments based on a family’s genetic history.
• Rare disease diagnosis. Healthcare IT solutions powered by AI will be able to identify rare genetic disorders that traditional diagnostic methods might overlook.
• Cancer research. AI is being used to analyze tumor mutations and identify the specific genetic drivers of a patient’s cancer.

AI-Driven Diagnostic Tools

The development of AI algorithms for diagnosis is a complex process that takes some time to show positive results. It all starts with the collection and curation of huge datasets. Quality is paramount here—the data needs to be accurate, clean, and well-organized. After you feed the algorithm with data, you need to employ supervised learning, which is generally more suitable for diagnosing, to train the algorithms. Over time, the algorithm learns to recognize specific features associated with various diseases and improves its diagnostic capabilities.

Deep learning is often used to power diagnostic tools as well. Due to neural networks with multiple layers, deep learning algorithms can analyze complex medical images, such as X-rays, MRIs, and CT scans. 

Undoubtedly, the use of AI-driven diagnostic tools improves both accuracy and speed. To make the right diagnosis, doctors need to review patient symptoms, medical histories, and a variety of tests (blood work, imaging scans, and biopsies), which are time-consuming and subject to human error. AI, however, can do that in a few minutes with a rather high precision. Other positive outcomes of AI tools for diagnosis include:

• Increased detection rates
• Reduced false positives and negatives 
• Streamlined workflows 
• Personalized medicine

Applications of AI in Precision Medicine

We have partially reviewed certain ways precision medicine AI companies apply artificial intelligence to customize treatments at scale for better patient outcomes. In fact, slightly over 40% of healthcare providers report that AI is already widely accepted and successfully integrated into their practices. As technology has started to bring gains, more organizations are considering AI outsourcing to embrace artificial intelligence. This widespread adoption can be attributed to AI’s numerous applications across different functions that enhance patient care.  

Personalized Treatment Plans

For decades, the medical approach to chronic diseases like diabetes, heart disease, and asthma often followed a broad-brush mindset. Precision medicine changes that and lets healthcare professionals deliver treatments and protocols unique to each individual. 

Let’s take a patient with cancer. Instead of undergoing a standard chemotherapy regimen that affects both cancerous and healthy cells, AI software can recommend a treatment plan that targets the specific genetic mutations that drive their tumor. 

The process begins with genomic sequencing, where the patient’s tumor DNA is sequenced to identify mutations and genetic anomalies. Fed into an AI system, it compares genomic data against massive databases of genetic information, clinical studies, and treatment outcomes. In such a way, AI finds the most effective treatments known to work against those particular mutations. Thus, it improves the effectiveness of the cancer treatment as well as reduces the trial-and-error period that often accompanies traditional methods. 

Generally speaking, AI lets providers predict individual responses to medication, develop personalized dosing regimens, and tailor treatment plans to lifestyle.

Here’s how personalized treatment plans make a difference:

• Improved outcomes. Treatments tailored to the individuals can dramatically improve health outcomes. For instance, a patient with hypertension will receive a treatment plan that considers their genetic predisposition, lifestyle, and even stress levels.
• Reduced side effects. Standard treatments often come with a range of side effects because they lack specificity and target both healthy and diseased cells. Precision medicine AI solves this issue by selecting medications and treatments that the patient is more likely to tolerate well, based on their genetic profile.
• Enhanced patient engagement. When patients receive care that is specifically designed for them, they feel that they are cared about and are more likely to engage with their treatment plan.
• Lower healthcare costs. While the initial cost of genetic testing and AI analysis might seem high, the long-term savings will be well worth it. Personalized medicine reduces the need for multiple doctor visits, unnecessary tests, and medication use. 
• Complication prevention. Early and proper intervention can prevent complications caused by chronic diseases. AI can identify early warning signs and recommend preemptive measures.

Drug Development and Discovery

Drug discovery has always been long and arduous. On average, it takes over a decade and costs billions of dollars to find the right molecular target, test countless drug candidates, and finally develop life-saving treatments. AI in precision medicine is a sure way to accelerate the process and make it less bank-breaking. Here’s how AI implementation streamlines drug development: 

• Identify targets. Through the analysis of genetic information, protein structures, and disease models, AI identifies novel drug targets – the molecules involved in disease processes. Instead of blindly testing a vast library of potential medications, researchers can focus their efforts solely on these promising targets with a higher chance of success.
• Screen drug candidates. AI virtually screens huge libraries of compounds and selects those with the most favorable properties for further investigation. It reduces the number of physical experiments needed and accelerates the discovery process severalfold. 
• Predict drug efficacy. Using data on drug interactions and patient responses, AI forecasts how a new drug candidate will behave in the human body. Through advanced simulations, AI can prognosticate a drug’s pharmacokinetics and pharmacodynamics—how it is absorbed, distributed, metabolized, and excreted. It lets researchers spot potential issues, such as toxicity or inefficacy, early on and save precious time and resources.
• Repurpose existing drugs. Sometimes, a drug developed for one condition can be effective for another, and AI can easily find that. Analysis of existing medications and their mechanisms of action allows AI to find potential new uses, which certainly cuts down development time. AI has already been successfully used for this task during the COVID-19 pandemic.

Predictive Care and Disease Prevention

Currently, healthcare reacts to illness. Let’s consider a typical scenario of Sarah, a 42-year-old with cardiovascular disease. Usually, the patient visits their doctor after experiencing symptoms like chest pain or shortness of breath. The doctor then runs a series of tests, diagnoses the condition, and prescribes treatments to manage the disease. In this situation, the disease has already progressed by the time it is detected.

Now, imagine the same Sarah in a system powered by precision medicine AI and predictive care. Long before Sarah experiences any symptoms, AI algorithms analyze her health data—genetic information, family medical history, lifestyle factors, and real-time data from wearable devices. The AI identifies patterns that suggest an elevated risk of cardiovascular disease.

Based on these insights, John’s healthcare provider can intervene early and create a personalized preventative plan that might recommend specific lifestyle changes and medications that reduce the risk of heart disease. 

As you can see, AI is a powerful tool that can enable predictive care through:

• Early identification of risk factors. Based on genetic data, AI detects individuals with a higher predisposition to specific diseases, which allows for preventative measures to be implemented before the disease takes hold.  
• Personalized risk assessment. AI takes an individual’s health profile (family history, environmental exposures, and current health status) into account to create a more nuanced picture of disease susceptibility.
• Disease progression monitoring. You can use AI to analyze medical data over time to track the progression of chronic diseases, which allows doctors to see any potential complications and adjust treatment plans accordingly. 

Precision Medicine AI: Final Thoughts

We can safely say that the future of AI in healthcare is bright, with the potential to redefine the way care is delivered. Precision medicine AI, possible at a large scale, is a colossal shift that moves us to personalized healthcare tailored to an individual’s unique biology and needs. Patients and hospitals alike will benefit from precision medicine.

If you, too, want to tap into the power of AI precision medicine for your practice, the time to act is now. Partnership with the right AI healthcare solution provider like Relevant will help you deal with data avalanches and adopt technology as painlessly as possible. 
As each human organism is unique, so is each case of AI implementation for a particular organization. You follow your workflows, have specific needs, and face certain challenges. That said, AI adoption for your precision medicine efforts requires a customized approach that aligns with your operational requirements and goals.

This means we help you assess your current systems, identify areas where AI can provide the most benefit, and implement technology to support your current practices while enhancing your capabilities. When you hire our AI engineers, they take care of all the tech aspects (data preparation, algorithm development) and help with a smooth transition to a new system. Contact us to take the first step to personalized care!